In this paper, we present a novel multiple kernel method to learn the optimal classification function for visual concept. Although many carefully designed kernels have been propose...
Setting up generic and fully transparent distributed services for clusters implies complex and tedious kernel developments. More flexible approaches such as user-space libraries ar...
Adrien Lebre, Renaud Lottiaux, Erich Focht, Christ...
Convolution kernels for trees provide simple means for learning with tree-structured data. The computation time of tree kernels is quadratic in the size of the trees, since all pa...
Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Ro...
Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in highdimensional feature spaces. Those interested in rela...
1 A kernel determines the inductive bias of a learning algorithm on a specific data set, and it is beneficial to design specific kernel for a given data set. In this work, we propo...